Geographical Analysis of media
5. Widgets
Objective
The aim of this section is to present the different widgets used for the exploration of hypercubes and developped during the ODYCCEUS project. We adapt a little the initial programs for the case of octocubes that are used in IMAGEUN but the principles remains the same. Each widget will export a dataframe and a plotly figure, making possible to store the results in javascript and/or to use the table for development with another software.
Preparation
Load multilevel octocubes and transform in hypercubes
We load the octocubes at different levels of time agregation and transform them in hypercubes by removing the dual dimensions of states and regions
base<-readRDS("octocubes/hc_mycorpus_covid_states_regions.RDS")
hc_day<-base$day[,.(tag=sum(tags),news=sum(news)),.(who,what,when,states=states1,regions=regions1)]
hc_week<-base$week[,.(tag=sum(tags),news=sum(news)),.(who,what,when,states=states1,regions=regions1)]
hc_month<-base$month[,.(tag=sum(tags),news=sum(news)),.(who,what,when,states=states1,regions=regions1)]
hc_year<-base$year[,.(tag=sum(tags),news=sum(news)),.(who,what,when,states=states1,regions=regions1)]Load statistical test function
#### ---------------- testchi2 ----------------
#' @title Compute the average salience of the topic and test significance of deviation
#' @name what
#' @description create a table and graphic of the topic
#' @param tabtest a table with variable trial, success and null.value
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest : Threshold of estimated value requested for chi-square test
testchi2<-function(tabtest=tabtest,
minsamp = 20,
mintest = 5)
{
tab<-tabtest
n<-dim(tab)[1]
# Compute salience if sample size sufficient (default : N>20)
tab$estimate <-NA
tab$salience <-NA
tab$chi2<-NA
tab$p.value<-NA
tab$estimate<-round(tab$success/tab$trial,5)
tab$salience<-tab$estimate/tab$null.value
# Chi-square test if estimated value sufficient (default : Nij* > 5)
for (i in 1:n) {
if(tab$trial[i]*tab$null.value[i]>=mintest) {
test<-prop.test(x=tab$success[i],n=tab$trial[i], p=tab$null.value[i],
alternative = "greater")
tab$chi2[i]<-round(test$statistic,2)
tab$p.value[i]<-round(test$p.value,5)
}
}
# }
return(tab)
}What
Function
### ---------------- what ----------------
#' @title Compute the average salience of the topic
#' @name what
#' @description create a table and graphic of the topic
#' @param hc an hypercube prepared as data.table
#' @param subtop a subtag of the main tag (default = NA)
#' @param title Title of the graphic
what <- function (hc = hypercube,
what = "what",
subtop = NA,
title = "What ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
tab<-tab[,list(news = sum(news)),by = what]
tab$pct<-100*tab$news/sum(tab$news)
p <- plot_ly(tab,
labels = ~what,
values = ~pct,
type = 'pie') %>%
layout(title = title,
xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
output<-list("table" = tab, "plotly" =p)
return(output)
}Application 1 : covid topic
res<-what(hc_year)
res$tableFALSE what news pct
FALSE 1: FALSE 647658 95.425272
FALSE 2: TRUE 31049 4.574728res$plotlyApplication n°2 : state subtopic
res <-hc_year %>% filter(states != "_no_") %>%
what(what = "states",
subtop ="RUS",
title = "Share of Russia in international news")
res$tableFALSE what news pct
FALSE 1: FALSE 142169.665 96.305252
FALSE 2: TRUE 5454.335 3.694748res$plotlyApplication n°3 : macroregion subtopic
res <-hc_year %>% filter(regions != "_no_") %>%
what(what = "regions",
subtop ="OR_EU",
title = "Share of EU in macroregional news")
res$tableFALSE what news pct
FALSE 1: TRUE 6191.583 32.02598
FALSE 2: FALSE 13141.417 67.97402res$plotlyWho.What
function
#### ---------------- who.what ----------------
#' @title visualize variation of the topic between media
#' @name who.what
#' @description create a table of variation of the topic by media
#' @param hc an hypercube prepared as data.table
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param title Title of the graphic
who.what <- function (hc = hypercube,
what = "what",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
title = "Who says What ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[,list(trial = sum(news),success=round(sum(news*what),0)),by = list(who)]
ref <-round(sum(tab$success)/sum(tab$trial),4)
tab$null.value<-ref
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
if (test==FALSE) {tab$index =tab$salience
tab$index[tab$index>4]<-4
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
}
else {tab$index=1-tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-rev(brewer.pal(7,"RdYlBu"))
mycol[4]<-"lightyellow"
}
p <- plot_ly(tab,
x = ~who,
y = ~estimate*100,
color= ~index,
colors= mycol,
hovertemplate = ~paste('Source: ',who,
'<br /> Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4)),
type = "bar") %>%
layout(title = title,
yaxis = list(title = "% news"),
barmode = 'stack')
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news.
res <- hc_year %>%
who.what(what = "what",
title = "Share of Covid in total news",
test=FALSE)
res$tableFALSE who trial success null.value estimate salience chi2 p.value
FALSE 1: DEU_suddeu 181869 8561 0.0457 0.04707 1.0299781 7.82 0.00258
FALSE 2: FRA_figaro 305544 12869 0.0457 0.04212 0.9216630 89.79 1.00000
FALSE 3: TUN_afrman 53182 4399 0.0457 0.08272 1.8100656 1670.02 0.00000
FALSE 4: TUR_dunya 138112 5220 0.0457 0.03780 0.8271335 197.69 1.00000
FALSE index
FALSE 1: 1.0299781
FALSE 2: 0.9216630
FALSE 3: 1.8100656
FALSE 4: 0.8271335res$plotlyApplication n°2 : State subtopic
An example of computation of the share of a national subtopic (Russia) in the sample of news where the topic is present (news with at least one state mentionned).
res <-hc_year %>% filter(states != "_no_") %>%
who.what(what = "states",
subtop ="RUS",
title = "Share of Russia in international news",
test=TRUE)
res$tableFALSE who trial success null.value estimate salience chi2 p.value index
FALSE 1: DEU_suddeu 21301 974 0.037 0.04573 1.2359459 45.27 0 1
FALSE 2: FRA_figaro 77104 2581 0.037 0.03347 0.9045946 26.80 1 0
FALSE 3: TUN_afrman 15816 174 0.037 0.01100 0.2972973 299.30 1 0
FALSE 4: TUR_dunya 33403 1726 0.037 0.05167 1.3964865 201.40 0 1res$plotlyApplication n°3 : Macroregion subtopic
Same example applied to macroregion : what is the share of the subtopic European Union in the subsample news where at least one macroregion is mentionned.
res <-hc_year %>% filter(regions != "_no_") %>%
who.what(what = "regions",
subtop ="OR_EU",
title = "Share of EU in macroregional news",
test=TRUE)
res$tableFALSE who trial success null.value estimate salience chi2 p.value
FALSE 1: DEU_suddeu 4019 1895 0.3203 0.47151 1.4720887 421.40 0.00000
FALSE 2: FRA_figaro 7455 2092 0.3203 0.28062 0.8761161 53.74 1.00000
FALSE 3: TUN_afrman 1458 453 0.3203 0.31070 0.9700281 0.57 0.77565
FALSE 4: TUR_dunya 6401 1752 0.3203 0.27371 0.8545426 63.61 1.00000
FALSE index
FALSE 1: 1.00000
FALSE 2: 0.00000
FALSE 3: 0.22435
FALSE 4: 0.00000res$plotlyWhen.What
function
#### ---------------- when.what ----------------
#' @title visualize variation of the topic through time
#' @name when.what
#' @description create a table of variation of the topic by media
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param title Title of the graphic
when.what <- function (hc = hypercube,
what = "what",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
title = "When is said What ?")
{
tab<-hc
tab$what<-tab[[what]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[,list(trial = sum(news),success=round(sum(news*what),0)),by = list(when)]
ref <-round(sum(tab$success)/sum(tab$trial),4)
tab$null.value<-ref
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
if (test==FALSE) {tab$index =tab$salience
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
}
else {tab$index=tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-brewer.pal(7,"RdYlBu")
mycol[4]<-"lightyellow"
}
p <- plot_ly(tab,
x = ~as.character(when),
y = ~estimate*100,
color= ~index,
colors= mycol,
# hoverinfo = "text",
hovertemplate = ~paste('Time: ',when,
'<br /> Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4)),
type = "bar") %>%
layout(title = title,
yaxis = list(title = "% news"),
barmode = 'stack')
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news by week for one media.
res <- hc_week %>% filter(who=="DEU_suddeu") %>%
when.what(what = "what",
title = "Share of Covid-19 topic in news published by Süddeutsche Zeitung",
test=FALSE)
res$tableFALSE when trial success null.value estimate salience chi2 p.value
FALSE 1: 2021-09-27 989 30 0.0471 0.03033 0.6439490 5.83 0.99211
FALSE 2: 2021-10-04 1217 57 0.0471 0.04684 0.9944798 0.00 0.50000
FALSE 3: 2021-10-11 1139 54 0.0471 0.04741 1.0065817 0.00 0.50000
FALSE 4: 2021-10-18 1168 50 0.0471 0.04281 0.9089172 0.39 0.73345
FALSE 5: 2021-10-25 1013 47 0.0471 0.04640 0.9851380 0.00 0.51256
FALSE ---
FALSE 257: 2022-11-28 136 5 0.0471 0.03676 0.7804671 0.13 0.64302
FALSE 258: 2022-12-05 167 4 0.0471 0.02395 0.5084926 1.51 0.89053
FALSE 259: 2022-12-12 161 1 0.0471 0.00621 0.1318471 5.12 0.98818
FALSE 260: 2022-12-19 91 5 0.0471 0.05495 1.1666667 NA NA
FALSE 261: 2022-12-26 86 7 0.0471 0.08140 1.7282378 NA NA
FALSE index
FALSE 1: 0.6439490
FALSE 2: 0.9944798
FALSE 3: 1.0065817
FALSE 4: 0.9089172
FALSE 5: 0.9851380
FALSE ---
FALSE 257: 0.7804671
FALSE 258: 0.5084926
FALSE 259: 0.1318471
FALSE 260: 1.1666667
FALSE 261: 1.7282378res$plotlyApplication n°2 : State subtopic
Example of analysis of the share of news about Russia among news mentionning one country, by month, for Le Figaro.
res <-hc_month %>% filter(states != "_no_") %>% filter(who=="FRA_figaro") %>%
when.what(what = "states",
subtop ="RUS",
title = "Share of Russia in international news published by Le Figaro",
test=TRUE)
res$tableFALSE when trial success null.value estimate salience chi2 p.value
FALSE 1: 2021-09-01 735 16 0.0335 0.02177 0.6498507 2.77 0.95205
FALSE 2: 2021-10-01 336 8 0.0335 0.02381 0.7107463 0.70 0.79830
FALSE 3: 2021-11-01 506 14 0.0335 0.02767 0.8259701 0.37 0.72759
FALSE 4: 2021-12-01 1008 43 0.0335 0.04266 1.2734328 2.34 0.06320
FALSE 5: 2022-01-01 330 20 0.0335 0.06061 1.8092537 6.67 0.00489
FALSE 6: 2022-02-01 422 37 0.0335 0.08768 2.6173134 36.60 0.00000
FALSE 7: 2022-03-01 482 92 0.0335 0.19087 5.6976119 363.84 0.00000
FALSE 8: 2022-04-01 399 55 0.0335 0.13784 4.1146269 130.97 0.00000
FALSE 9: 2020-07-01 1054 36 0.0335 0.03416 1.0197015 0.00 0.48696
FALSE 10: 2020-08-01 985 20 0.0335 0.02030 0.6059701 4.90 0.98655
FALSE 11: 2020-09-01 1395 36 0.0335 0.02581 0.7704478 2.32 0.93606
FALSE 12: 2020-10-01 1371 33 0.0335 0.02407 0.7185075 3.48 0.96894
FALSE 13: 2020-11-01 1346 25 0.0335 0.01857 0.5543284 8.81 0.99850
FALSE 14: 2020-12-01 1367 40 0.0335 0.02926 0.8734328 0.63 0.78693
FALSE 15: 2021-01-01 1235 24 0.0335 0.01943 0.5800000 7.12 0.99619
FALSE 16: 2021-02-01 1304 28 0.0335 0.02147 0.6408955 5.46 0.99028
FALSE 17: 2021-03-01 1516 28 0.0335 0.01847 0.5513433 10.12 0.99927
FALSE 18: 2021-04-01 1095 40 0.0335 0.03653 1.0904478 0.22 0.31804
FALSE 19: 2021-05-01 1254 27 0.0335 0.02153 0.6426866 5.18 0.98861
FALSE 20: 2021-06-01 1254 50 0.0335 0.03987 1.1901493 1.38 0.11987
FALSE 21: 2021-07-01 1189 32 0.0335 0.02691 0.8032836 1.40 0.88132
FALSE 22: 2021-08-01 930 26 0.0335 0.02796 0.8346269 0.72 0.80187
FALSE 23: 2022-05-01 1307 80 0.0335 0.06121 1.8271642 30.14 0.00000
FALSE 24: 2022-06-01 1454 79 0.0335 0.05433 1.6217910 18.85 0.00001
FALSE 25: 2022-07-01 1284 65 0.0335 0.05062 1.5110448 11.10 0.00043
FALSE 26: 2018-01-01 1357 28 0.0335 0.02063 0.6158209 6.55 0.99475
FALSE 27: 2018-02-01 1261 46 0.0335 0.03648 1.0889552 0.26 0.30515
FALSE 28: 2018-03-01 1453 104 0.0335 0.07158 2.1367164 63.89 0.00000
FALSE 29: 2018-04-01 1493 66 0.0335 0.04421 1.3197015 4.96 0.01297
FALSE 30: 2018-05-01 1577 51 0.0335 0.03234 0.9653731 0.03 0.57380
FALSE 31: 2018-06-01 1615 47 0.0335 0.02910 0.8686567 0.83 0.81939
FALSE 32: 2018-07-01 1600 50 0.0335 0.03125 0.9328358 0.19 0.66666
FALSE 33: 2018-08-01 1493 40 0.0335 0.02679 0.7997015 1.87 0.91444
FALSE 34: 2018-09-01 1460 33 0.0335 0.02260 0.6746269 5.02 0.98750
FALSE 35: 2018-10-01 1661 36 0.0335 0.02167 0.6468657 6.81 0.99548
FALSE 36: 2018-11-01 1436 45 0.0335 0.03134 0.9355224 0.15 0.64884
FALSE 37: 2018-12-01 1130 26 0.0335 0.02301 0.6868657 3.52 0.96976
FALSE 38: 2019-01-01 1733 52 0.0335 0.03001 0.8958209 0.55 0.77085
FALSE 39: 2019-02-01 1508 30 0.0335 0.01989 0.5937313 8.21 0.99791
FALSE 40: 2019-03-01 1628 28 0.0335 0.01720 0.5134328 12.86 0.99983
FALSE 41: 2019-04-01 1691 36 0.0335 0.02129 0.6355224 7.41 0.99677
FALSE 42: 2019-05-01 1779 43 0.0335 0.02417 0.7214925 4.50 0.98303
FALSE 43: 2019-06-01 1779 64 0.0335 0.03598 1.0740299 0.26 0.30351
FALSE 44: 2019-07-01 1759 52 0.0335 0.02956 0.8823881 0.73 0.80277
FALSE 45: 2019-08-01 1590 66 0.0335 0.04151 1.2391045 2.91 0.04408
FALSE 46: 2019-09-01 2008 57 0.0335 0.02839 0.8474627 1.47 0.88714
FALSE 47: 2019-10-01 1929 39 0.0335 0.02022 0.6035821 10.10 0.99926
FALSE 48: 2019-11-01 1574 47 0.0335 0.02986 0.8913433 0.54 0.76806
FALSE 49: 2019-12-01 1302 44 0.0335 0.03379 1.0086567 0.00 0.50000
FALSE 50: 2020-01-01 1667 24 0.0335 0.01440 0.4298507 18.20 0.99999
FALSE 51: 2020-02-01 1347 30 0.0335 0.02227 0.6647761 4.90 0.98660
FALSE 52: 2020-03-01 1288 19 0.0335 0.01475 0.4402985 13.41 0.99987
FALSE 53: 2020-04-01 1258 21 0.0335 0.01669 0.4982090 10.46 0.99939
FALSE 54: 2020-05-01 1295 42 0.0335 0.03243 0.9680597 0.02 0.55420
FALSE 55: 2020-06-01 1397 28 0.0335 0.02004 0.5982090 7.40 0.99675
FALSE 56: 2022-08-01 968 59 0.0335 0.06095 1.8194030 21.69 0.00000
FALSE 57: 2022-09-01 1322 102 0.0335 0.07716 2.3032836 76.47 0.00000
FALSE 58: 2022-10-01 1198 75 0.0335 0.06260 1.8686567 30.45 0.00000
FALSE 59: 2022-11-01 1037 52 0.0335 0.05014 1.4967164 8.37 0.00191
FALSE 60: 2022-12-01 953 45 0.0335 0.04722 1.4095522 5.12 0.01180
FALSE when trial success null.value estimate salience chi2 p.value
FALSE index
FALSE 1: 0.95205
FALSE 2: 0.79830
FALSE 3: 0.72759
FALSE 4: 0.06320
FALSE 5: 0.00489
FALSE 6: 0.00000
FALSE 7: 0.00000
FALSE 8: 0.00000
FALSE 9: 0.48696
FALSE 10: 0.98655
FALSE 11: 0.93606
FALSE 12: 0.96894
FALSE 13: 0.99850
FALSE 14: 0.78693
FALSE 15: 0.99619
FALSE 16: 0.99028
FALSE 17: 0.99927
FALSE 18: 0.31804
FALSE 19: 0.98861
FALSE 20: 0.11987
FALSE 21: 0.88132
FALSE 22: 0.80187
FALSE 23: 0.00000
FALSE 24: 0.00001
FALSE 25: 0.00043
FALSE 26: 0.99475
FALSE 27: 0.30515
FALSE 28: 0.00000
FALSE 29: 0.01297
FALSE 30: 0.57380
FALSE 31: 0.81939
FALSE 32: 0.66666
FALSE 33: 0.91444
FALSE 34: 0.98750
FALSE 35: 0.99548
FALSE 36: 0.64884
FALSE 37: 0.96976
FALSE 38: 0.77085
FALSE 39: 0.99791
FALSE 40: 0.99983
FALSE 41: 0.99677
FALSE 42: 0.98303
FALSE 43: 0.30351
FALSE 44: 0.80277
FALSE 45: 0.04408
FALSE 46: 0.88714
FALSE 47: 0.99926
FALSE 48: 0.76806
FALSE 49: 0.50000
FALSE 50: 0.99999
FALSE 51: 0.98660
FALSE 52: 0.99987
FALSE 53: 0.99939
FALSE 54: 0.55420
FALSE 55: 0.99675
FALSE 56: 0.00000
FALSE 57: 0.00000
FALSE 58: 0.00000
FALSE 59: 0.00191
FALSE 60: 0.01180
FALSE indexres$plotlyApplication n°3 : Macroregion subtopic
Example of analysis of the share of news about European news among news mentioning one macroregion, by year, for Dunya.
res <-hc_year %>% filter(regions != "_no_") %>% filter(who=="TUR_dunya") %>%
when.what(what = "regions",
subtop ="OR_EU",
title = "Share of EU in macroregional news published by Dunya",
test=TRUE)
res$tableFALSE when trial success null.value estimate salience chi2 p.value index
FALSE 1: 2019-01-01 918 265 0.2737 0.28867 1.0546949 0.96 0.16346 0.16346
FALSE 2: 2020-01-01 1713 422 0.2737 0.24635 0.9000731 6.31 0.99399 0.99399
FALSE 3: 2021-01-01 1448 350 0.2737 0.24171 0.8831202 7.29 0.99654 0.99654
FALSE 4: 2022-01-01 1235 346 0.2737 0.28016 1.0236025 0.23 0.31653 0.31653
FALSE 5: 2018-01-01 1087 369 0.2737 0.33947 1.2402996 23.32 0.00000 0.00000res$plotlyWhere.What
function
#### ---------------- where.what ----------------
#' @title visualize spatialization of the topic
#' @name where.what
#' @description create a table of variation of the topic by media
#' @param hc an hypercube prepared as data.table
#' @param test : visualize test (TRUE) or salience (FALSE)
#' @param minsamp : Threshold of sample size requested for salience computation
#' @param mintest sample size of estimate for chi-square test (default = 5)
#' @param map a map with coordinates in lat-long
#' @param proj a projection accepted by plotly
#' @param title Title of the graphic
where.what <- function (hc = hypercube,
what = "what",
where = "where",
subtop = NA,
test = FALSE,
minsamp = 20,
mintest = 5,
map = world_ctr,
proj = 'azimuthal equal area',
title = "Where said What ?")
{
tab<-hc
tab$what<-tab[[what]]
tab$where<-tab[[where]]
if (is.na(subtop)){tab$what <-tab$what !="_no_"}else {tab$what <- tab$what == subtop}
# {tab$what <-tab$what !="_no_"}
tab<-tab[,list(trial = round(sum(news),0),success=round(sum(news*what),0)),by = list(where)]
ref <-round(sum(tab$success)/sum(tab$trial),4)
tab$null.value<-ref
tab<-testchi2(tabtest=tab,
minsamp = minsamp,
mintest = mintest)
tab<-tab[order(-chi2),]
if (test==FALSE) {tab$index =tab$salience
tab<-tab[tab$trial > minsamp,]
mycol<-brewer.pal(7,"YlOrRd")
} else {tab$index=tab$p.value
tab<-tab[tab$trial*tab$null.value>mintest,]
mycol<-brewer.pal(7,"RdYlBu")
mycol[4]<-"lightyellow"
}
map<-merge(map,tab,all.x=T,all.y=F,by.x="ISO3",by.y="where")
#map2<-map[is.na(map$pct)==F,]
#map2<-st_centroid(map2)
#map2<-st_drop_geometry(map2)
g <- list(showframe = TRUE,
framecolor= toRGB("gray20"),
coastlinecolor = toRGB("gray20"),
showland = TRUE,
landcolor = toRGB("gray50"),
showcountries = TRUE,
countrycolor = toRGB("white"),
countrywidth = 0.2,
projection = list(type = proj))
p<- plot_geo(map)%>%
add_markers(x = ~lon,
y = ~lat,
sizes = c(0, 250),
size = ~success,
# color= ~signif,
color = ~index,
colors= mycol,
# hoverinfo = "text",
hovertemplate = ~paste('Location: ',NAME,
'<br /> Total news : ', round(trial,0),
'<br /> Topic news : ', round(success,0),
'<br /> % observed : ', round(estimate*100,2),'%',
'<br /> % estimated : ', round(null.value*100,2),'%',
'<br /> Salience : ', round(salience,2),
'<br /> p.value : ', round(p.value,4))) %>%
layout(geo = g,
title = title)
output<-list("table" = tab, "plotly" =p)
return(output)
}Applicaton n°1 : Covid Topic
An example of computation of the share of a non spatial topic (Covid) in the full sample of news by week for one media.
world_ctr<-readRDS("map/world_ctr_4326.Rdata")
res <- hc_week %>% filter(who=="DEU_suddeu") %>% filter(states !="DEU") %>%
where.what(what = "what",
where ="states",
title = "Share of Covid-19 topic in foreign news published by Süddeutsche Zeitung",
test=FALSE)
#res$table
res$plotlyApplication n°2.1 : Regional subtopic
Example of analysis of the share of news about EU in news associated to one country
res <-hc_month %>% filter(states != "_no_") %>%
filter(who=="FRA_figaro") %>%
filter(states!="FRA") %>%
where.what(what = "regions",
subtop = "OR_EU",
where = "states",
title = "Countries associated to EU by Le Figaro",
test=TRUE,
mintest=2,
minsamp=10)
#res$table
res$plotlyApplication n°2.2 : Regional subtopic
Example of analysis of the share of news about Africa in news associated to one country
res <-hc_month %>% filter(states != "_no_") %>%
filter(who=="TUR_dunya") %>%
filter(states!="TUR") %>%
where.what(what = "regions",
subtop = "OR_EU",
where = "states",
title = "Countries associated to EU by Dunya",
test=TRUE,
mintest=1,
minsamp=5)
#res$table
res$plotlyApplication n°2.3 : Regional subtopic
Example of analysis of the share of news about EU in news associated to one country
res <-hc_month %>% filter(states != "_no_") %>%
filter(who=="DEU_suddeu") %>%
filter(states!="DEU") %>%
where.what(what = "regions",
subtop = "OR_EU",
where = "states",
title = "Countries associated to EU by Süd. Zeit.",
test=TRUE,
mintest=2,
minsamp=10)
#res$table
res$plotlyApplication n°2.4 : Regional subtopic
Example of analysis of the share of news about Africa in news associated to one country
res <-hc_month %>% filter(states != "_no_") %>%
filter(who=="TUN_afrman") %>%
filter(states!="TUN") %>%
where.what(what = "regions",
subtop = "OR_EU",
where = "states",
title = "Countries associated to EU by African Manager",
test=TRUE,
mintest=1,
minsamp=5)
res$plotly